1. Background and Overview

For much of the post-war period, Australia's economy was relatively insulated
from international trade. In part, insulation reflected the costs arising from
distance. However, the high levels of assistance provided to
import-competing manufacturing during, and after World War II, also
played an important role. Taken as a share of GDP, gross trade declined from
the relatively high levels it had earlier achieved (Figure 1). By the
1960s, when trade levels in the other industrial countries were booming, Australia's
trade share seemed unreasonably low
(Kuznets 1959).
Sheltered behind protective walls, manufacturing expanded rapidly (Figure 2),
with an industrial structure characterised by high levels of concentration,
as a small number of firms shared the relatively small domestic market. Each
of these firms (the larger ones frequently being foreign owned) produced too
broad a range of products, using plants too small to ever achieve economies
of scale, making what were intended to be promising ‘infant industries’
into premature geriatrics. Market disciplines being weak, managerial slack
was pervasive, as was the sharing of rents through the system of centralised
wage determination. All of this contributed to the slow rate of productivity
growth identified as a central concern by the Vernon Committee and by the first
OECD survey of Australia.

By the early 1990s this characterisation seemed increasingly out of date, if not
completely dated. Gross trade as a percentage of GDP (both expressed in real
terms) rose from just under 25 per cent in 1972/73 to around
39 per cent in 1992/93. This increase paralleled substantial reductions
in protection, with the effective rate of assistance to manufacturing reduced
by two-thirds (from 35 per cent in 1972/73 to around 12 per cent 20 years later).
The former adviser to the Hawke Government, Ross Garnaut, has written that
the transformation was ‘bigger than the end of the British Corn Laws
that earned Peel and Cobden a dozen pages in our high school history books,
eight or ten time zones and over a century away’ (Garnaut 1991). And
though this statement contains an element of exaggeration, the extent of the
reforms remains startling in historical
perspective.[1]

Underpinning the change was the government's conviction that ‘suppliers
of goods and services which are protected from international competition […]
are not subject to the pressures which ensure efficient management and production’
(One Nation 1992). And undoubtedly, the changes must have had substantial impacts
on the ways in which manufacturing firms work and the efficiency with which
they use resources. Ultimately these changes should allow sustainable increases
in living standards.

However, to date there is little evidence of a significant increase in the trend
rate of productivity growth. Figure 3 presents data on labour productivity
in the non-farm sector for the period from 1978/79 to 1993/94. The
mid 1980s productivity slowdown was probably influenced by the extended
period of wage moderation, which reduced capital-labour substitution. However,
the path which succeeded it cannot, at this stage, be said to be above that
of the past.

That it should be difficult to detect unambiguous traces of the influence of structural
change on productivity growth is not surprising, for at least three reasons.
First, short-run productivity trends reflect the interaction of a large number
of cyclical and structural forces that cannot readily be
disentangled.[2]
Second, even in looking at the longer run, when the instruments of growth accounting
can be deployed, economists have rarely been able to satisfactorily explain
more than half of the observed change in
output.[3]
Finally, it is reasonable to hypothesise that the economy is still in the adjustment
process, with changes occurring that will only really show up in the aggregates
sometime down the road.

Given these difficulties it is important to examine the changes underway at a micro-level
– that is, to assess the extent to which, and the ways in which, adjustment
is proceeding both within, and between, industries. This is the primary task
of this paper. To do this, we draw on a new data source, namely a survey of
Australian manufacturers carried out by the Australian Bureau of Statistics
(ABS) on behalf of the
Australian Manufacturing Council (AMC).

The analysis in the paper addresses three specific questions:

How far has the process of international integration gone? Is Australia now integrated
into the world economy to an extent comparable to that of other small, industrial
countries?

How has integration affected the pattern of resource allocation between industries,
notably within manufacturing? To what degree has Australian manufacturing
become more specialised?

Within industries, to what extent, and through what channels, has
economic integration affected productive efficiency?

The paper's main findings can be summarised as follows. First, though the trade
intensity of the Australian economy is still below the average for the industrial
economies, the gap has diminished in recent years and, the extent to which
it persists, largely reflects Australia's distance from major markets
(Section 2).

Second, rapid internationalisation has been associated with greater specialisation
in trade and output within manufacturing, and with accelerated structural change
in the pattern of employment (Section 3).

Third, though the data do not allow us to examine changes in the behaviour of individual
enterprises over time, there is strong cross-sectional evidence of international
exposure affecting behaviour and performance within industries. These effects work partly through intensified competition,
which typically leads to increased product quality and reduces intra-industry
disparities in performance. But the main impacts come from involvement with
foreign markets, as it is outward orientation, rather than market circumstances,
which is most strongly associated with superior performance (Section 4).

In terms of the links between international exposure and firm performance we hypothesise
three major effects: international exposure will encourage greater learning,
as firms come into contact with, and measure themselves against, a broader
range of rivals; it will force managers to tackle inherited inefficiencies;
and it will encourage greater selection (as weaker firms are forced to adjust
or decline). The results in Section 4 provide some support for these hypotheses
and suggest the following:

Involvement in the international economy provides firms with expanded opportunities
to learn. Firms more heavily involved with the international economy (be it
through exporting or through foreign direct investment) are more likely than
are domestically-oriented firms to: systematically measure themselves up against
world-best practice; focus on improving product quality and customer satisfaction;
and successfully learn from customers and suppliers. All of these activities
feed into productivity gains in the medium term.

International involvement is also associated with greater efforts to tackle many
of the rigidities which have long characterised the Australian economy. Though
internationally-oriented firms are most likely to regard the current industrial
relations arrangements as constraining, they also report greater success in
implementing enterprise agreements that work, and are more likely to regard
unions as playing a positive role in their plants.

These differences in characteristics are associated with substantial differentiation
within industries. The factors which most sharply distinguish the better performing
firms are investment in intangible assets (mainly skills and R&D), less
conflictual industrial relations, and a more systematic emphasis on monitoring
their performance relative to rivals. Importantly, there remains a very substantial
tail of firms – accounting for nearly 20 per cent of manufacturing employment
– which does not export, carries out little or no R&D, and seems
to make no investment in monitoring its competitive position. Nearly
half of these firms supply intermediate inputs, so that their performance
could act as a substantial constraint on the competitiveness of their clients.
The disparities in performance tend to be greatest within industries with
above average concentration and benefiting from high levels of assistance.
Moreover, we find evidence of growing differentiation and specialisation
within industries, as competition sorts out the good performers from those
which lag.

Finally, some of the policy implications of these results are explored in Section 5.

2. Australia's Internationalisation in Comparative Perspective

The extent to which countries are integrated into the world economy depends partly
on policy variables – such as the extent of protection – and partly
on structural variables, such as size and distance from major markets. Since
the pioneering work of Hirschman (1945), Maizels (1963) and Kuznets (1959),
a substantial body of literature has developed on the way each of these variables
contributes to shaping patterns of openness and trade. Drawing on the hypotheses
developed in this literature, it is possible to examine empirically whether
in the past, Australia was less integrated with the world economy than might
have been expected on the basis of the structural variables alone; and equally,
to assess the extent to which any such gap may have diminished in recent years.
Subsequent sections carry out this analysis first for international trade and
then for international investment.

2.1 International Trade

The ratio of gross trade (imports plus exports) to GDP is conventionally used as
a measure of openness to international trade, though it is perhaps better (and
more neutrally) described a measure of trade
intensity.[4]
Following Kuznets and Hirschman, it seems reasonable to suppose that four factors
will affect the value this measure takes for a particular country. These are:

A rise in per capita GDP will, on balance, tend to increase trade intensity as it
is generally associated with greater demand for diversity. So long as product
differentiation entails fixed costs, an increase in
per capita GDP, holding everything else constant, will involve a higher
level of imports and hence of exports.

Higher GDP, on the other hand, will tend to be associated with reduced trade intensity,
since at a given level of GDP per capita, larger economies will be able to
satisfy a higher share of their needs through internal sources, thereby reducing
transport costs.

Greater proximity to potential markets increases trade intensity by reducing the
natural protection which comes from the cost of transport and communications,
and by diminishing the cultural distance separating market participants.

Table 1 sets out the results of a regression incorporating these variables, together
with a dummy variable for Australia. The model has been estimated for 1975,
1980, 1985 and 1990 over a panel of 56 countries accounting for the vast bulk
of world income. While a more detailed discussion of sources and methods is
provided in Appendix A, it is clear from the table that –
with the exception of the (generally insignificant) protection variables for
the industrial economies – all the coefficients on the Hirschman-Kuznets
variables have the expected sign and are strongly significant. These results,
together with those in Table 2, suggest the following:

Table 1: Trade Intensity Equation

(dependant variable: log of trade intensity)

1975

1980

1985

1990

Australian dummy

−0.148 (0.304)

−0.108 (0.293)

−0.050 (0.263)

0.068 (0.291)

Real GDP

−0.220** (0.021)

−0.220** (0.021)

−0.220** (0.021)

−0.220** (0.021)

Real GDP per capita

0.234** (0.065)

0.234** (0.065)

0.234** (0.065)

0.234** (0.065)

Proximity to world production

0.002** (0.001)

0.002** (0.001)

0.002** (0.000)

0.002** (0.000)

Protection × (D1+D2)

−0.001 (0.010)

−0.013 (0.076)

−0.025 (0.106)

0.041 (0.247)

Protection × (D3)

−0.493 (0.329)

−0.593** (0.201)

−0.557* (0.247)

0.078 (0.388)

Protection × (D4)

−0.664** (0.228)

−0.441** (0.153)

−0.406** (0.138)

−0.323* (0.130)

D1 {Industrial Countries}

1.983** (0.628)

2.115** (0.625)

2.033** (0.617)

1.809** (0.630)

D2 {Western Hemisphere}

1.941** (0.537)

1.975** (0.540)

1.848** (0.526)

1.812** (0.532)

D3 {Africa, Middle East, Other Europe}

2.302** (0.548)

2.412** (0.548)

2.282** (0.551)

1.946** (0.576)

D4 {Asia}

2.751** (0.521)

2.863** (0.521)

2.646** (0.524)

2.682** (0.540)

0.68

0.70

0.75

0.68

Notes: (a) Trade intensity is equal to . (b) Real
GDP and real GDP per capita are in logs. (c) Standard errors are
in parentheses. (d) * (**) denote coefficients which are significant
at the 5% (1%) significance level. (e) The protection variable is
represented by three variables which allows for slope variation. (f) The test statistic for the restrictions that the coefficients on real GDP and
real GDP per capita are equal across time is Χ2
(6) = 4.212. The null hypothesis cannot be rejected at the 1% significance
level. These restrictions have been imposed.

Table 2: Trade Intensity Ratios(a)

1960

1965

1970

1975

1980

1985

1990

Australia

31.5

31.3

28.9

28.8

33.9

35.3

34.5

United States

9.4

9.4

11.3

16.1

20.8

17.1

21.1

Canada

36.0

38.4

42.9

47.2

55.1

54.5

50.5

Germany

35.3

35.6

40.3

46.5

53.3

61.5

58.4

France

26.9

25.8

31.1

36.9

44.3

47.2

45.3

OECD(b)

47.6

47.5

52.4

56.2

63.9

68.5

63.8

World

45.3

44.8

47.6

55.7

61.1

60.0

61.0

Notes: (a) The trade intensity ratio is defined as the sum of exports and
imports as a proportion of GDP (all in current prices). (b) The trade intensity ratios for the OECD and the ‘world’
are calculated as the simple average for the respective group.

Source: Penn World Table (Mark 5.5), ‘OPEN’ variable.

Australia's trade intensity is well below the average for the OECD and the world
as a whole, and has been so for the entire period covered in Table
2.[5] The gap
opened up in the late 1960s, as the trade intensity of the Australian economy
actually fell, whilst for most other countries it continued to rise. More
recently, Australia's trade intensity has risen, whilst that of the OECD
and the larger group of countries appears to have plateaued.

The rise in Australia's trade intensity between 1975 and 1990, although partly
explained by the structural determinants of trade intensity, can be mainly
attributed to a decline in the degree to which Australia is peculiar. Although
imprecisely estimated, the Australia dummy was negative, large and economically
significant in 1975. By 1990, this was no longer the case.

The remaining gap between Australian and average OECD trade intensity is still substantial
in absolute terms, being equivalent to almost 30 per cent of 1990 GDP. However,
it is largely attributable to the variable capturing proximity to world production.
If Australia was located as close to the centre of world production as France,
the estimates suggest that its trade intensity in 1990 would be more than
twice as large, at 73 per cent of GDP.

Though the Asian countries tend to trade especially heavily, as is apparent from
the intercept dummy for Asia, their trade intensities are also significantly
influenced by trade protection. While the coefficient on the protection variable
has been diminishing over time, its continuing weight highlights the gains
in terms of expanded world trade, and presumably incomes, that could accrue
from further liberalisation in the region.

2.2 Foreign Direct Investment

Increases in trade intensity have been paralleled by a continued rise in foreign
direct investment (FDI). Substantial difficulties are involved in comparisons
of FDI between countries and over time. These difficulties arise because of
differences in the treatment of retained earnings, in the valuation bases used,
in the treatment of debt and in the control thresholds used for defining foreign
ownership. As a result, the international data, and notably the series collected
by the IMF and the OECD, are not fully comparable. They can, nonetheless, be
suggestive of broad trends and it is in this spirit that they are examined
here.

Expressed in current prices, cumulated FDI inflows to Australia were some five times
greater during the period
1981–1992 than during 1973–1980 (a period of unusually low
FDI
inflows).[6]
Comparing these
two sets of years, Australia's share of OECD inward direct investment
– that is, the ratio of FDI inflows to Australia, to all FDI inflows
to countries in the OECD area – remained roughly constant at around 5
per cent, but out of a strongly rising total.

At the same time, there has been a shift in the sectoral allocation of FDI away from
manufacturing, mining and agriculture towards the service industries. In particular,
manufacturing's share of cumulated FDI fell from
35 per cent in 1976–81 to 27 per cent in 1982–92. This may
partly reflect the impact of reductions in assistance on the incentive to ‘leap
over the tariff wall’ which earlier studies found to be an important
component of FDI into Australia. It is also no doubt related to the falling
significance of manufacturing in the economy as a whole. Nevertheless, foreign-owned
firms still account for a large proportion of activity in the manufacturing
sector. The latest ABS figures, which refer only to 1986/87, show that the
share of foreign-owned firms in manufacturing value added has remained fairly
stable at around 31 per cent since the mid
1970s.[7]

In contrast to the mixed picture for the inward flows, Australia's share of OECD
outflows of FDI – that is, its importance as a home country for FDI –
increased markedly during the time period considered. Taking the
period 1973–1980, Australia accounted for less than 1 per cent
of cumulated outflows of FDI from OECD countries. This more than doubled to
just under 2 per cent for the period 1981–1992. Australia's FDI outflows
in this latter period exceeded those of Canada and Switzerland, traditionally
substantially larger foreign investors than Australia, and were barely smaller
than those of Sweden.

The extent and pattern of the increase in Australian FDI can be examined using US
Department of Commerce data on FDI inflows into the United States. While Australian
firms accounted for barely 0.5 per cent of annual inflows of FDI into the United
States in 1980 (and even less before then), their share quadrupled to between
2 and
2.5 per cent for 1985, 1986, 1987 and 1988 and then seemed to stabilise
at around 1.5 per cent in 1990 and 1991. As with foreign trade, this substantial
rise points to the shrinking, and perhaps even disappearance of a gap between
the actual level of Australian FDI and that which might be expected from an
economy with Australia's characteristics.

Much as with trade intensity, this hypothesis can be analysed by considering the
factors that are likely to influence a country's share in FDI inflows into
a particular host market. Drawing on the Dunning-Caves ‘eclectic’
model of foreign investment, it can be hypothesised that this share will be
associated with: the size of the home country's economy; its distance from
the market in question; the home country's level of technological and managerial
sophistication as reflected in per capita income and in its share of cumulated
OECD area R&D expenditures; as well as an exchange rate variable capturing
the familiar Aliber effects.

A regression model estimated on this basis (with county shares of FDI inflow into
the US over the period
1976–1992 as the dependent variable) explained some 36 per cent
of the variance in the data, though all the coefficients were statistically
significant at the one per cent level. The results of this model suggest that
the Australian share was significantly below the expected level in the period
to the mid 1980s, and significantly above it from then
on.[8]

2.3 Summary

Three major results can be drawn from the data presented above.

First, though the trade intensity of the Australian economy remains below that of other
OECD countries, the gap has fallen substantially in recent years, and could
fall further as a result of income growth in Australia's region, reductions
in transport and communications costs, and cuts in protection in the Asian
economies. Second, Australia remains relatively open to foreign direct investment,
and has attracted continuing substantial inflows of FDI despite reduced
incentives for simple import substitution. Third, Australian outflows of FDI
increased very markedly over the course of the 1980s – indeed, to a point where
(at least on the basis of US data) they exceeded the levels which might have
been expected given Australia's economic size and structure. Combined,
these results highlight the continued rapid internationalisation of Australia's
economy.

3. Output and Resource Shifts Between Industries

The closer integration of the Australian economy into world markets can be expected
to affect efficiency by altering the allocation of resources between industries, and by changing conduct and performance within
industries. The former corresponds to the familiar mechanisms of Ricardian
comparative advantage; the latter, though it has long been referred to in studies
of international trade and investment, has only very recently been given a
firmer analytical basis. These two, by no means mutually exclusive, types of
effects are considered respectively in this section and in the next.

The factors and processes underlying changes in the pattern of output and resource
use are well known. They centre on the changes in relative product prices associated
with increased international integration, which should alter the structure
of the economy and shift resources between industries. In principle, since
protection has been reduced rather than removed, the effects on welfare are
difficult to gauge a priori. Nonetheless, it seems reasonable to assume that
reductions in assistance of the magnitude observed should be welfare increasing,
as the shifts in resources in line with comparative advantage result in the
transfer of resources from less to more valued uses, raise productivity measured
at world prices and increase national income (see Dowrick in this Volume).

The data available do point to inter-industry shifts within manufacturing. By and
large, these shifts have resulted in greater specialisation, both in terms
of output and trade.

Output specialisation can be examined by assessing trends in the sectoral distribution
of value added, and by measuring the degree to which a few industries dominate
manufacturing net output. As a result of natural and policy protection, the
Australian manufacturing sector has traditionally been far less specialised
than its counterparts in other small industrial economies, most strikingly
those in Europe. However, the gap in this respect between Australia and comparable
countries overseas has diminished somewhat in recent years.

The relevant data are set out in Figure 4. The OECD's STAN database, which provides
value-added data for
26 industry groups, has been used to calculate a Herfindahl index of
manufacturing industry value added, with higher values of the index implying
a higher degree of specialisation. The data cover the period from 1970
to 1989 (observations for some countries are available up to 1991).
Increases in the value of the index for Australia occurred in the early 1970s, in
the late 1970s and then in the period from 1985. At the end of
the 1980s, the value of the index for Australia was similar to that
for Canada, but was still well below that for the small open European economies.

These is also some evidence of increasing specialisation in international trade.
Two indicators have been examined in this respect.

The first uses series on trade and output corrected for the double counting of own-industry
intermediate inputs. (These series are described in
Appendix B.)
These data are used to calculate ratios of import penetration, and export orientation,
in volume terms for the 12 ASIC 2-digit manufacturing industries. The results,
set out in
Figure 5, point to increased inter-industry dispersion, in the sense
that the standard deviation of the measures rise over time. However, because
the means for both series increased even more rapidly, the unweighted coefficients
of variation have actually tended to decline.

The second indicator of trade specialisation examined is the Balassa index of revealed
comparative advantage
(RCA).[9]
The data are drawn from the STAN database and use trade data reclassified to the
26 STAN manufacturing industry groups. Table 3 sets out the summary statistics
for the RCA estimates for 13 OECD countries, including Australia, for 1979
and 1990 (the last year for which data are available).

In both years, the median RCA for Australia has been the lowest among the countries
considered. Given the way the index is normalised, this points to a relatively
skewed pattern of trade. This is borne out by the values of the indicators
of skewness (third moment) and kurtosis (fourth moment). A major factor here
are the very high RCAs for manufactures that are not elaborately transformed,
notably Non-Metallic Minerals and Food, Beverages and Tobacco. Only Finland,
with its concentration on exports of pulp and paper, has a similar pattern.

The degrees of skewness and of kurtosis have also tended to rise over time. Sharp
increases during the 1980s in the RCA index for Non-Metallic Minerals appear
to have been a major contributor to this change. A regression analysis of the
RCAs on time shows that RCAs have also increased significantly for Petroleum
Products, Textiles and (though to a lesser extent) Iron and Steel, and have
declined significantly for Food, Beverages and Tobacco, Chemicals (excluding
drugs) and Metal Products, while remaining stable, or displaying no clear trend,
for the other industry groups.

Specialisation in trade and output have been accompanied by shifts in the pattern
of resource use. Figure 6 sets out Lawrence indices of structural change calculated
annually over the period from 1975 to
1994.[10]
The results are presented first for manufacturing, and then for the entire
non-farm economy (but keeping the
13 manufacturing subdivisions separate). Though this measure can be
distorted by oscillations in industry shares, a broadly similar picture emerges
when rolling 5-year-ended data are used. The results point to an increase in
the year-on-year rate of structural change in manufacturing, with an especially
marked rise in the extent of the shifts in employment.

However, at least on a preliminary analysis, it is not apparent that these shifts
are any larger than those experienced in other small industrial economies,
as the need to reverse the legacy of a long period of protection might have
suggested. For example, an indicator of structural change derived from the
OECD's ISDB
database[11]
suggests a significantly greater stability of industry shares in Australian manufacturing
employment than observed in Canada, Sweden or the
UK.[12]
A similar picture emerges from the ISDB data on capital stocks, though greater doubts
must be expressed about the reliability of these estimates.

Moreover, we find no relationship between the pattern of output change and the extent,
or direction of changes, in revealed comparative advantage. Going by the data
on Australian manufacturing in STAN for the
period 1970–89, there was a statistically significant association
between changes in RCAs and changes in output shares for only 3 of the 22 industry
groups.

This suggests some caution about the extent and pattern of inter-industry shifts
in resource use. Three factors may be at work.

First, protection remains significant for some of the industry groups which, were
they fully exposed to international competition, might experience the largest
shrinkages. This is notably the case for passenger motor vehicles and for textiles,
clothing and footwear (TCF) – and indeed, the ISDB data indicate that
Australia has experienced smaller declines in employment in TCF over the past
two decades than most other OECD economies (Figure 7).

Second, change in the composition of employment may have been slowed by the extended
period of wage restraint in the 1980s. Much as appears to have happened in
the United States, low rates of wages growth may have offset the adjustment
pressures which economic integration would otherwise have placed on low-productivity
sectors.

Third, even in those sectors where adjustment is now underway, the transition period
may extend well into the future. The current slow rate of change is not necessarily
a reliable indicator of longer-term outcomes.

In short, further shifts in the pattern of resource use within manufacturing may
still lie ahead. However, it would be wrong to think that these shifts are
inevitable. After all, predictions of pure specialisation in line with comparative
advantage and determined by factor proportions can only be derived on the basis
of strong simplifying assumptions. It is increasingly recognised that, under
conditions of imperfect competition, the patterns of specialisation which emerge
from free trade may bear little resemblance to those expected from Ricardian
models of comparative advantage. Indeed, in recent work which extends the general
equilibrium (GE) model to include oligopoly, and then derives GE models of
trade under imperfect competition, the equilibrium pattern of output is largely
indeterminate (Gabszewicz and Michel 1992; Cordella 1993). In these models,
most of the effects of integration arise from changes in behaviour within industries – a result not inconsistent with the long-term
trends characterising the advanced economies.

Some indication of the relative importance of inter as compared with intra-industry
shifts can be obtained by comparing the contributions to manufacturing labour-productivity
growth of changes within industries on the one hand, (the upper panel in Figure 8)
and of shifts in the allocation of labour between industries on the other (the
lower panel in the same
figure).[13]
In virtually all instances, the first dominates the outcome; this suggests
that it is within industries, rather than in intersectoral reallocation,
that the greatest effects of international integration will be found.

4. Changes in Performance Within Industries

If the major benefits of trade reform are concentrated within industries, we would
hope to find some evidence of the link between the international environment
and firm behaviour using firm-level data. To this end, this section begins
by examining the analytical reasons for expecting such a relationship; then
draws on these reasons to develop testable hypotheses; and finally sets these
hypotheses against data drawn mainly from the AMC survey.

4.1 The Analytical Background

Increased international integration is likely to affect efficiency at the level of
the firm and the industry primarily through its impact on the intensity of
competition. By reducing price-cost margins, notably in concentrated industries,
greater competition will yield improvements in allocative efficiency; but it
may also increase technical efficiency – that is, the productivity with
which resources are used. It is the latter which is the prime concern of this
paper and recent, largely theoretical work identifies three mechanisms through
which it may be affected by changing product market conditions.

4.1.1 Yardstick efficiency

The first of these is the impact of product market conditions on agency costs –
that is, on the costs owners face in ensuring that managers have adequate incentives
to maximise shareholder value. The underlying notion is that in a more competitive
market, owners can more readily compare the performance of the firms in which
they have invested, to the performance of other firms. This allows them to
discriminate between say, low profits due to industry-wide demand shocks and
low profits due to managerial slack or to rent-sharing between managers and
workers. As a result, owners can better structure the incentives managers face,
securing a closer alignment between managerial actions and shareholder objectives.
This will reduce managerial slack, and ensure that prior inefficiencies are
wound back.

Two points are worth noting. The first is that greater monitoring by owners of the
comparative performance of managers can be expected to lead the managers themselves
to invest more heavily in comparing their performance with that of managers
in other firms. Second, just as competition increases the ability of owners
to monitor their agents, it makes it easier for managers to monitor the performance
of the firm's other employees. As a result, intensified competition should
be reflected in the tighter assessment of performance at all levels of organisation.

4.1.2 Sampling efficiency

Though yardstick efficiency depends on the basic factors determining profits within
a firm being highly correlated across
firms[14]
(since this is what allows managerial performance to be compared), product market
competition may also yield efficiencies when the firms within an industry differ
in important respects. In particular, if firms are viewed as ‘taking
bets’ on particular ways of doing things, having a greater number of
firms in a market will, all other things being equal, accelerate the rate at
which the most efficient approaches are discovered. To the extent that there
are spillover effects (that is, the firms in an industry can learn from each
other, for example through the yardstick effects of benchmarking), experimentation
will increase efficiency, not only in the innovating firm, but also across
the firm population as a whole.

Greater international exposure can be a particularly effective means of enhancing
this process of ‘letting a thousand flowers bloom’. It provides
for a vast increase in the number of sampling points, as domestic firms are
exposed to the example of firms overseas. Inward investment by foreign firms
can also be used to ‘show-case’ technologies, organisational approaches
and marketing techniques not in widespread use in the domestic market (though
it can also have the effect of accentuating the barriers to entry confronting
domestic entrepreneurs). In addition, through exporting to, and investing in,
foreign markets, domestic firms may become more aware of foreign sources of
technology. Finally, and perhaps especially significantly, gains may come from
access to a broader range of intermediate inputs and capital goods. In addition
to its immediate cost-reduction impact, the expansion in goods available, effectively
lowers the costs of innovation. This effect will extend to the non-traded sector
as well, which thereby benefits directly from increased integration into the
world
economy.[15]

4.1.3 Selection efficiency

In addition to effects at the level of the firm, increased product-market competition
will alter the process by which inefficient firms are ‘weeded out’
and efficient firms are rewarded. The presumption here is that firms are indeed
asymmetric, and that superior performance cannot be costlessly imitated. Stronger
product-market competition is then presumed to result in the more rapid and
complete sorting of firms into distinct performance classes, with the less
productive firms being forced to exit the market. The most natural route through
which this Darwinian process occurs is the reduction in price-cost margins
brought by increased competition, since this will make it more difficult for
inefficient firms to
survive.[16]
At the same time, owners, now better able to compare performance, are not likely
to continue funding inefficient firms, while potential employees, mindful of
the costs of redundancy, may become more wary of accepting jobs in the firms
least likely to survive. As a result, inefficient firms will face tighter price
and cost constraints, making their continued existence less likely.

4.1.4 Caveats

While each of these factors suggests that intensified product-market competition
will be associated with increased technical efficiency, a number of caveats
are worth noting.

First, the dynamics of adjusting to greater competition may be complex and costly.
In particular, firms that are faced with the likelihood of exit may have strong
incentives to curtail investment and raise prices, especially when their more
efficient rivals realise that this is merely a transitory end-game strategy.
Under these circumstances, it can be a profit-maximising strategy for the likely
survivor to slow their own expansion (as they wait for exit to occur), so that
the overall price level for the industry actually rises. The prediction that
industry efficiency will be enhanced may then be realised only very slowly.

Second, even putting the adjustment dynamics aside, the precise features of the ‘equilibrium’
towards which the system is heading are not necessarily as straightforward
as the above discussion suggests. This is especially so when firms can choose
to compete through sunk investments such as outlays on advertising, R&D,
or the holding of inventories and other forms of excess capacity. In these
cases, in which sunk costs are endogenous, the expected outcomes depend very
heavily on the precise characterisation of the competitive process. For example,
where competition occurs largely through advertising, an increase in the threat
of competition (due say, to an expansion of the market) may lead to a rise in costs, as incumbents increase outlays so as to protect their
market position.

Equally, where fixed costs are large and sunk (for example, because production requires
capital goods that have few alternative uses and a finite and relatively predictable
lifetime), reduced barriers to new competition are not necessarily associated
with greater productive efficiency, since they may result in excess entry and
over-capacity. Last, but by no means least, is the Schumpeterian conjecture,
according to which some degree of allocative inefficiency – that can
only be sustainable if competition
in the market is imperfect – is needed if firms are to make
the investments required to compete for the
market.[17]

In each of these cases, the prediction that greater competition will increase productive
efficiency may not hold true, at least over some possibly significant range
of the intensity of competition scale.

4.2 Implementation and Hypothesis Formulation

While the mechanisms reviewed above cannot easily be set against empirical evidence,
they can be used to develop testable hypotheses. These hypotheses fall into
three broad groups.

4.2.1 Integration, learning and performance

A first set of hypotheses relates integration, learning and performance.

Agency cost models of the firm, briefly described above, provide a starting point.
In these models, intensified competition increases the incentives and capabilities
of owners to strike efficient contracts with managers. Significant here is
the ability of owners to make a broader range of comparisons between firms,
and hence more readily distinguish good from poor managerial
performance.[18]
Given this link, it would seem reasonable to expect that managers, faced with an
expanded set of competitor firms, will themselves have increased incentives
to systematically monitor the behaviour of rivals and compare corporate strategies
and performance. This suggests a number of hypotheses.

First, within industries, the firms most likely to engage in benchmarking are those
which face the lowest costs in acquiring competitor information, for example,
because they can spread the fixed costs of doing so over greater size; and/or
because they are integrated into company groupings – such as multinational
enterprises – that can secure comparative performance data from internal
sources.

Second, there may also be an effect by which it is the ‘better’ managerial
teams that make the greatest investment in securing comparative information,
both because they stand to lose less from doing so and are better placed to
act on the information they acquire.

Third, the incentives to engage in systematic comparisons may be greatest in larger
firms, these being the firms for which agency costs are likely to be highest
in the first place, and which, in the absence of international competition,
are most likely to lack adequate domestic comparators.

In summary, there should be a relationship between firm and industry characteristics,
the adoption of systematic processes of monitoring rivals, and corporate performance
(for example, in terms of competitiveness on world markets). This relationship
will be reinforced by the ‘sampling’ effects of greater product-market
competition. In particular, entry by importers and the greater exposure of
domestic firms to export markets will bring a larger range of alternative approaches
and strategies into play. So too should contact with a more extensive set of
customers and suppliers, who can act as valuable sources of market information
and of technical support. All of this should result in accelerated learning,
most notably by the firms directly involved in international trade but also,
through spillover effects, by other firms in the industry.

4.2.2 Corporate practices, selection and efficiency

By tightening the product-market constraints bearing on managers, integration will
also alter managers' abilities and incentives to perpetuate inefficient
ways of doing things. Given the historical development of Australian manufacturing,
four areas are likely to be especially important.

First, inefficient work practices are likely to come under pressure, as are the other
mechanisms by which rents are shared between managers and other employees.
One would therefore expect to see two effects jointly: managerial slack being
reduced as agency costs are reduced; and equally, employee slack decreasing
as a result of reductions in agency costs within firms. Tighter product-market
constraints – a dwindling of the rents available for sharing –
should make this process all the sharper.

Third, excess product variety, with its corollary of sub-scale production, could
be perceived as a greater handicap. In effect, though product differentiation
can dissuade new entrants, it is unlikely to be a successful strategy when
it imposes a substantial cost disadvantage on domestic producers. The very
broad product ranges typical of Australian industries could be expected to
prove unsustainable in a more competitive environment.

Fourth, and interacting with the third point above, greater access to export opportunities,
which effectively expands the market available to producers, should strengthen
the incentives to exploit economies of scale. A larger market creates greater
room for efficiently-scaled plants. The trade-off between carrying excess capacity
at the time new plants are first introduced, and achieving lower unit costs
through economies of scale over time, will tend to favour larger plants when
the absolute size of the market is larger, given an independently determined
growth rate of demand and producers acting on a stable pattern of oligopolistic
interaction (Scherer et al. 1975). At the same time, access to export markets
will tend to encourage more aggressive capacity expansion by low-cost producers.
By allowing these producers a greater range of opportunities to displace less-efficient
rivals, not only at home, but also abroad, it reduces the price fall necessary
to accommodate the additional output their expansion entails.

Together, these factors should be reflected in a pattern in which the more efficient,
export-oriented producers take the lead in seeking to implement new industrial
relations arrangements, as well as in trying to secure the fullest benefit
from economies of scale and scope.

4.2.3 Productivity and specialisation

The process within each industry which results from these forces should have four
salient features.

First, as the most efficient producers self-select by leading in the adoption of
more efficient ways of working, there might be, at least initially, a rising,
possibly substantial, gap between firms within industries.

Second, the greater the barriers to the diffusion of new management practices, and
the higher the costs of exit, the larger and more persistent this gap will
be. We might, therefore, expect to find the convergence process within industries
being slowest for industries or market segments where sunk costs are relatively
high.

Third, given these disparities, the firms which perform best – in terms of
productivity and competitiveness on world markets – should be those which
have the greatest commitment and ability to learn. They would, in other words,
bring together the factors set out above: orientation to benchmarking, and
adoption of processes for systematically monitoring cost and quality relative
to competitors; willingness to learn from foreign suppliers of technology and
inputs; capacity to create an industrial relations climate sufficiently flexible
to adapt to new ways of doing things; and access to the resources needed to
implement change.

Finally, as this sorting process runs its course, Australian industries could become
more specialised, reflecting not only the fuller exploitation of product-specific
economies of scale, but also (and probably more importantly) managerial diseconomies
of scope. Given a continuing (indeed, income-elastic) demand for diversity,
rising
intra-industry
specialisation should result in greater intra-industry trade.

4.3 Testing

These hypotheses have been tested first by using the responses to the recent AMC
survey and second, by analysing industry-level data on trade and output.

The survey was conducted over December/January 1993/94 and hence results may have
been affected by either shutdowns over the Christmas period, or by seasonal
work, for example, in the food industry. Aimed at firms with more than 20 employees,
the survey was stratified across 12 ASIC/ANZSIC industry codes and three size
categories (by employees: 20–49, 50–99 and over 100). Sampling
frames were designed to ensure that all
36 cells had a minimum number of respondents. Overall, there were 962
respondents to the survey, equivalent to over 10 per cent of the population,
sufficient to provide an adequate basis for analysis.

The survey contained over 100 questions, many of them involving scalar judgments
(that is, the respondents were asked to rank themselves on a scale). It is
consequently a very rich but complex database, with especially difficult problems
being involved in disentangling the causal links between variables.

Given the number of questions, and the fact that some of the terms used in the survey
may have been unfamiliar to the respondents, there is some concern that the
quality of the results may have been affected by respondent fatigue. Two approaches
were used to test for this:

First, where similar questions have been asked in different parts of the questionnaire
(in particular, where a question has been asked near the beginning of the
questionnaire, when respondents are freshest, and a similar question is asked
near the end), the correlation between responses has been examined.

Second, where the tone of a question is different from that of the questions surrounding
it (for example, because the surrounding questions involve replies where a
higher value is ‘better’ than a lower value, while the reply structure
for the question at issue goes in the other direction), the correlation between
answers has been checked to see if the respondent was ‘awake’
to the change in scale.

The results of this analysis are reproduced in Table 4. As can be seen, for all the
questions testing fatigue, except that on marketing costs (where the two questions
asked are the least similar of those considered), there is a positive association
in
responses.[19]
Equally, the questions involving a change in the direction of a scale exhibit
a negative association. It therefore seems reasonable to conclude that the
length and complexity of the survey did not seriously erode the quality of
the replies.

Table 4: Tests of Respondent Consistency

Type of question

Survey references

Kendall's tau

Standard error

Tests of ‘fatigue’:

Operations focused

PL6 and TE2

0.19

(0.03)

Just-in-time

FO1B and TE1P

0.53

(0.03)

Production quality

MS3F and PO1F

0.24

(0.03)

New products

MS3H and PO1G

0.29

(0.03)

Materials

MS3D and PO1A

0.13

(0.03)

Marketing

MS3K and PO1D

−0.03

(0.03)

Tests of ‘awareness’:

Technology/HRM

MS4C and MS4D

−0.16

(0.03)

HRM/simultaneous

MS4D and MS4E

−0.11

(0.03)

The quality of the responses can also be tested by examining the concordance between
firms' ranking of their performance on Likert-type scales with the rankings
which emerge from comparisons of quantitative performance indicators. For example,
firms were asked whether they considered themselves to be among the technological
leaders in their industry. About 8 per cent of respondents rated themselves
in this group. Analysis of these responses shows that the firms in this group
do tend to score more highly on a range of performance indicators: they are
more likely to export (about 46 per cent versus a 36 per cent average for all
firms); and they do more R&D. Further, it is worth noting that these firms
also rated themselves as having higher productivity growth rates and levels,
were more likely to be foreign owned and were more likely to benchmark their
performance against rivals (the association between a firm's rating of
its ‘technological lead’ and these other variables was tested using
Kendall's tau, all associations being significant at the one per cent level).
This last point is especially significant because it suggests that the firms
involved were relatively well-informed.

Potential response bias was investigated through a telephone survey of 108 firms
that had not responded to the original postal survey. The results, which are
reported in AMC (1994), found that non-respondents had higher self-assessed
scores across a sub-sample of 8 questions drawn from the original postal survey.
While this may be taken to imply that there is a non-response bias against
the ‘better’ firms, it may also indicate a tendency to be overly
optimistic in responses to telephone surveys. Nevertheless, the potential for
some biases in this direction must be noted in interpreting the results presented
below.

On balance, all of this gives some support to the view that the assessments reported
in the survey are of reasonable quality, and these are consequently used below
for statistical testing. For simplicity, in the presentation of the results
below, the actual values of Kendall's tau, and its asymptotic standard
error, are omitted. However, unless otherwise noted, the results reported are
significant at the one per cent level.

4.3.2 The changing intensity of competition

An important element in the hypotheses set out above is the effect of greater integration
on the intensity of product-market competition, which then alters firm conduct
and performance.

However, the survey provides little indication of the competitive conditions in which
firms operate. Firms were asked to report their market share – which
even at the best of times is a poor indicator of market power – but there
was a high non-response rate to this question, and those firms which responded
appear to have done so using quite different conceptions of the relevant market.
As a result, indicators of competitive conditions had to be derived from other
sources. Estimates have been made of the trade-adjusted Herfindahl-Hirschman
indices (HHIs) of concentration at the industry level using the methodology
set out in Appendix C; but these will overstate market power
in industries where entry barriers are low, and may understate it where markets
are geographically fragmented.

These problems extend to assessing the degree to which firms are, or feel, constrained
by international competition. Since the survey does not contain specific questions
in this regard, appropriate indicators have had to be constructed. Two are
especially important.

The first are time series on import penetration (see Figure 5, above). In particular,
an effort has been made to develop a time series of import penetration at the
industry level corrected for the consumption of own-industry intermediate inputs
(methods and main results are described in Appendix B). Though these measures are an improvement
on those normally used, they still have serious
weaknesses.[20]

Second, the import-penetration measures have been supplemented by using measures
of manufacturers' perceptions of the intensity of import competition. The
primary source is the quarterly Survey of Australian Manufacturing carried out by the AMC since June
1989, the main results of which are summarised in Table
5.[21]
The survey asks firms whether they believe import competition has increased in the
last quarter, and hence responses are likely to be quite sensitive to
exchange rate conditions. Three points can be drawn from the data set out in
the table:

Table 5: Perceptions of Increased Import Competition

(annual average of quarterly net balance of percentages of respondents)

1989/90

1990/91

1991/92

1992/93

1993/94

Food, beverages and tobacco

11.7

9.3

13.3

5.3

4.0

Textiles

12.5

15.8

17.7

27.3

31.3

Clothing and footwear

32.0

35.8

40.3

49.5

27.7

Wood products

2.8

16.5

14.5

11.8

9.0

Paper products

4.3

9.3

8.3

9.3

3.7

Chemical and petroleum products

15.9

20.3

29.6

21.8

16.3

Non-metallic mineral products

10.2

2.8

15.2

12.8

15.7

Basic metal products

1.2

18.5

22.4

19.5

10.7

Fabricated metal products

15.0

12.4

11.2

13.5

7.7

Transport equipment

21.9

11.8

12.3

8.3

5.3

Other machinery and equipment

20.8

23.7

19.8

22.8

19.0

Miscellaneous manufacturing

18.4

24.2

20.6

12.5

9.7

Total manufacturing

12.9

15.3

17.7

15.8

11.7

Note: 1993/94 data are estimates based on three-quarters to March.

Source: Australian Manufacturing Council, Survey of Australian Manufacturing.

The consistently positive numbers indicate that competition from imports has been
continuously increasing since 1989, despite the fact that this post-dates
the largest declines in protection.

The greatest increases appear to have occurred in Clothing, Chemical and Petroleum
Products, and
Basic Metals. Transport Equipment, in contrast, has relatively low
figures, which may reflect both continued protection and the fact that many
of its imports are controlled by the domestic producers.

The fall in some of the measures in 1993 may be due to the weaker exchange rate.

Interestingly, there is little correlation between the series on import penetration
and that on perceptions of the intensity of import competition. This suggests
that the distinction between arms-length imports on the one hand, and related-party
imports on the other, may be significant in explaining differing degrees of
product-market contestability.

For example, as shown in Figure 9, in the Paper Products, Transport Equipment, and
Basic Metal Product industries, the proportions of firms responding that import
competition has increased are lower than one might expect given the observed
change in import penetration. This may indicate the relatively small proportion
of competing imports in these industries. In contrast, perceptions of increased
import competition appear greater than actual increases in penetration in the
Chemical and Petroleum Products and Non-Metallic Mineral Products industries.
This may reflect greater degrees of contestability in these
markets.[22]

4.3.3 Integration, learning and performance

Given this background, four results associating internationalisation, learning and
corporate performance emerge with some strength from the work carried out to
date.

First, the firms most likely to systematically monitor the performance of their
rivals are those most engaged in the international economy.

This is suggested by examining the responses to questions about whether the firm
has mechanisms in place to benchmark its performance relative to competitors
and, if so, how much time senior management devotes to this task. Replies to
these questions show that export-focussed firms (that is, firms listing a foreign
market as among their top two priorities) are more likely to benchmark (58
per cent having policies to this effect as against
40 per cent of the remaining group). In contrast, regionally-focussed
firms (that is, firms selling largely within their own State), are less likely
to benchmark. In addition, US and Japanese-owned firms, which presumably can
secure high quality comparative information from internal sources, are far
more likely to benchmark than any other category of firm, including those which
are Australian-owned.

The results also show that larger firms make greater use of benchmarking than their
smaller counterparts, possibly reflecting a higher incentive to benchmark (given
higher agency costs in the absence of systematic performance comparisons) and
the fixed costs involved in securing competitive information. Other than through
international involvement, we find little relation between the frequency of
benchmarking and the competitive conditions in which firms operate.

Second, firms engaged in the international economy are also most likely to focus on
customer satisfaction and on product quality.

Exporters gave a higher score to the five questions measuring the effort devoted
to monitoring customer satisfaction than did other firms. This is also true
of foreign-owned firms. Exporters were also more likely to focus on controlling
product quality, with significant positive relationships on all but one of
the four variables in the survey aimed at capturing the investment firms make
in this respect. Here too, the links primarily work through exporting, with
the intensity of competition having little effect on performance.

Third, being engaged in international markets is also the primary factor which allows
firms to extend their sources of information and learning.

Regardless of competitive conditions in the industry, exporters are more likely than
domestically-oriented firms to find their customers to be of at least some
assistance in achieving world-best practice (64 per cent versus
56 per cent for non-exporters). Exporters are also far more likely than
other firms to respond that overseas firms have been an important
source of assistance. The relationship persists even after accounting for the
role of customers and suppliers, probably reflecting the impact of exposure
to a broader range of competitors.

Foreign-owned firms are more likely to regard their parents as a source of technical
assistance than are Australian firms owned by multi-unit parents. However,
even removing the impact of assistance from foreign parents, there remains
a strong association between foreign ownership and assistance from foreign
firms. This may reflect the impact of management structures in foreign subsidiaries,
which perhaps provide for more effective learning. Also important may be the
higher R&D intensity of foreign firms, which is likely to increase their
information about best practice and reduce the costs involved in identifying
overseas sources of technical help.

It is, however, worth noting that subsidiaries of foreign firms, though they regard
their parent as a valuable source of advice, also frequently regard it as a
barrier to achieving improved performance (37 per cent of foreign firms regard
their parent as a barrier – compared with 23 per cent of the domestic
firms owned by multi-firm entities). Importantly, the two views are often held
jointly – 38 per cent of the foreign firms that found their parents of
assistance also regarded them as a barrier. This suggests that the barriers
in question may involve restrictions on the use in export markets, of the skills
and capabilities obtained from the parent. We return to this point below.

Fourth, learning and the international diffusion of technological progress are also
likely to have been assisted by the declines in protection of imported capital goods.

The Industries Commission estimates that the effective rate of assistance (ERA) on
imports of industrial machinery and equipment (taking account of concessions
for imported capital) fell from over 40 per cent at the start of the 1970s,
to around 13 per cent by 1992/93. This process is especially important because
it directly benefits some parts of the non-traded sector which are large importers
of such equipment – the ABS estimates that more than half of the gross
fixed capital expenditure of firms in the finance, property and business services
and wholesale trade sectors is imported. Arguably, the diffusion of technological
progress through its embodiment in capital goods is likely to be more important
in imports of advanced technology. Table 6 shows the importance of imports
to 22 categories of advanced manufacturing technologies. Imports were the source
of over half of such items in all but one of the technology classes. Furthermore,
the importance of imports as a source of such technology seems to be increasing.

In short, involvement with international markets does appear to be associated with
greater and more systematic learning. It is highly likely that in practice,
the causality runs both ways, creating positive feedback loops to the benefit
of outward-oriented firms.

4.3.4 Corporate practices, selection and efficiency

Improvements in management incentives and information, combined with the rigours
of a more demanding selection environment, should put intensified pressure
on inherited inefficiencies. Out of the broad range of results derived from
analysing the survey, three can be used to examine these impacts.

First, firms' efforts to upgrade their product quality appear to be related to
their involvement in the international economy.

The survey asked firms to rate their product defect rate relative to competitors,
and to report the share of defective products in their product volume. Using
the responses, we classified firms into two groups: those with low product
quality and those with high product quality. We then estimated a probit model
using variables from the survey as explanatory variables. The results are reported
in Table 7. A positive coefficient indicates that the relevant variable makes
it more likely that the firm is a high-product-quality firm.

Table 7: Quality Equation

Variable

Relative defect rate

Defects/volume

Constant

0.11

(0.29)

0.42

(1.13)

Industry dummies:

Food, beverages and tobacco

0.30

(1.40)

0.44

(2.15)

Textiles

−0.22

(−0.71)

−0.37

(−1.16)

Clothing and footwear

0.32

(1.31)

0.23

(1.00)

Wood products

0.67

(2.84)

0.18

(0.80)

Paper products

0.02

(0.11)

−0.09

(−0.42)

Chemical and petroleum products

−0.41

(−1.54)

0.34

(1.32)

Non-metallic mineral products

−0.31

(−1.02)

−0.56

(−1.69)

Basic metal products

−0.49

(−1.46)

−0.09

(−0.32)

Fabricated metal products

−0.18

(−0.91)

0.47

(2.38)

Transport equipment

0.14

(0.52)

0.52

(1.92)

Other machinery and equipment

−0.17

(−0.76)

0.57

(2.67)

Foreign ownership

0.42

(2.81)

0.27

(1.85)

Parent restrictions

−0.19

(−1.26)

−0.98

(−6.50)

R&D/sales

−0.002

(−0.07)

−0.14

(−3.97)

Size

−0.27

(−3.67)

−0.26

(−3.78)

Export

0.06

(0.49)

0.12

(1.04)

Adopted any advanced technology

—

—

−0.23

(−1.79)

Customer focus in design

−0.22

(−1.35)

−0.52

(−3.31)

Measure quality

0.20

(2.18)

0.09

(1.04)

Standardised procedures

0.15

(2.55)

0.09

(1.58)

Pay-for-performance scheme

0.002

(1.36)

−0.0008

(−0.68)

Number of trade unions

−0.07

(−1.56)

−0.03

(−0.80)

Number of quality inspectors

−1.70

(−1.86)

0.73

(0.85)

Frequent review of cost of quality

−0.08

(−1.70)

−0.07

(−1.47)

Frequent review of customer satisfaction

0.05

(1.00)

0.13

(3.06)

Likelihood ratio

76.64

167.72

Per cent correctly predicted

64.86

69.91

Note: t-statistics are in parentheses.

The results suggest that firms in industries with higher levels of protection tend
to have lower product quality, as do those in industries which have recently
experienced large reductions in protection. Size also tends to be associated
with lower relative product quality, as is industrial concentration measured
by the estimated HHI.

Foreign ownership is the single variable most strongly associated with higher product
quality. However, those subsidiaries which report that they are subject to
parent-company restrictions limiting their competitiveness (the ‘parent
restrictions’ variable in the model) tend to have significantly higher
product defect rates, possibly as a result of foregone learning economies.
Some management practices are also associated with higher product quality,
notably the efforts the firm makes to measure product quality, the extent to
which it has standardised procedures for controlling quality, and the frequency
with which it reviews customer satisfaction.

The negative and significant coefficients on the number of quality inspectors and
on frequency of review of the cost of product quality, and equally negative
but not significant coefficients for R&D intensity and customer focus in
design, suggest that these measures may have been introduced in response to
problems with quality in the
first place.
The coefficient on exporting is also positive but not significant.

Second, the firms most oriented to international markets seem to be among the leaders in
industrial relations reform.

In a probit model explaining exports (discussed in greater detail below), there is
a positive association between export involvement and having an enterprise
agreement. Moreover, exporters are much more likely to rate their enterprise
agreement as effective, as well as to respond that unions have a positive role
in their plants – possibly reflecting greater willingness by unions to
cooperate in the face of tighter product market constraints. Nonetheless, exporters
are more likely to regard the current industrial relations system as a constraint
on their performance.

A similar pattern holds for foreign-owned firms, which, like exporters, are more
likely to have an enterprise agreement, more likely to regard it as effective,
and more likely to view unions as having a positive role in their plants. Foreign-owned
firms appear slightly more likely to introduce gain-sharing, productivity-related
pay and piece rates. This may be related to the fact that these firms use different
technologies from their Australian-owned counterparts, and notably seem to
make greater use of advanced manufacturing techniques.

Third, there is some, albeit mixed, evidence linking protection to excess product variety,
though less so to foregone economies of scale.

Firms were asked whether they thought they produced too many product varieties or
perceived their size to be a barrier in competition. As far as product variety
is concerned, the sectoral pattern is complex but suggestive. Transport Equipment,
despite its high levels of protection, has the lowest rate of respondents considering
excess product variety to be a problem. This may well reflect the incentives
which have been provided under the Passenger Motor Vehicle Plan to reduce the
number of models each firm produces in Australia. Partly as a result of these
incentives, the number of Australian-made models has fallen from 13 in 1985
to 6 today. Once the Transport Equipment industry is removed, the relationship
between the level of the effective rate of assistance and the frequency with
which firms in an industry report excess variety as a problem, becomes significantly
positive.

An equally complex pattern emerges in respect of economies of scale. Fewer than 30
per cent of firms regard themselves as handicapped by the scale of their operations.
Interestingly, the highest proportion of these is in chemicals, which has relatively
low ERAs now but was highly protected until the late 1970s. Since that time,
the industry has experienced relatively slow rates of domestic demand growth
and sharp increases in import penetration, which may have limited the ability
to exploit scale economies. There is also some clustering of positive responses
in the highly protected clothing and footwear industries, possibly capturing
firms' perception that long-run survival will depend on their ability to
offset a labour-cost disadvantage through greater scale economies. However,
firms producing transport equipment and textiles do not consider themselves
to be sub-scale.

Overall, in a probit model including size, foreign ownership, whether the parent
company imposed barriers on the affiliate, an estimate of the Minimum Efficient
Scale (MES) and whether firms are automated, an increase in the ERA increases
the likelihood of scale being viewed as a barrier but the coefficient is not
significant.[23]

In considering these results, it is worth noting the uncertainties which surround
the extent and significance of scale economies.

Recent studies find, for example, that even in industries in which scale economies
seem substantial, there is considerable entry by firms operating far below
MES (Acs and Audretsch 1988, 1989). Though Schumpeterian selection may ensure
that some of these firms disappear while others eventually expand to MES, it
also seems to be the case that a not insignificant proportion survive while
remaining below the MES threshold. This suggests that scale penalties are either
smaller than the conventional MES estimates suggest and/or can be offset by
other factors such as superior product quality, higher market flexibility and
better customer service.

Moreover, technical change may be reducing MES in many industries. Though the evidence
is largely anecdotal, the hypothesis gets some support from overseas trends
in the size distribution of firms. A similar pattern emerges for Australia,
as can be seen from the evidence on the changing size distribution of Australian
plants presented in Figure 10. In every industry, the average number of employees
per firm has fallen over the past 15 years, in many cases substantially. Furthermore,
the distribution of employment per firm has become more positively skewed as
the number of small firms has increased. This may reflect rapidly rising labour
productivity in the larger firms (which could imply an increase in the MES);
but it seems difficult to believe that there would be so uniform a trend away
from employment in larger plants if substantial parts of Australian manufacturing
were seriously sub-scale. The ‘conventional wisdom’ that Australian
manufacturing plants are too small may consequently need to be re-examined.

All of this highlights the many and diverse respects through which international
involvement alters corporate conduct; the key issue then is how this translates
into differences in performance. Six results of the analysis are worth emphasising
here.

First, there appears to be a rising dispersion in performance within industries.

This rising dispersion is most evident in the information available on labour productivity
(data on capital stocks are not available for individual firms). In particular,
we have used data on employment and output to compute average productivity
levels for each size-class of firms, as a basis for calculating the coefficient
of variation of within-industry
productivity.[24]
The results show that for almost all of the 12 2-digit industries the coefficient
of variation increased over the period 1978–1992. This result also emerges
from a regression of the survey data on productivity levels
(together with other variables) against the survey responses on productivity
growth rates, yielding a significant positive coefficient. It can
be supposed that this reflects the differing capacity of firms to make the
transition to a more open environment; but as the disparities open up, they
may well be accentuated by the more frequent shocks, notably resulting from
supply-side
innovations,[25]
which can be expected to characterise a competitive, internationally exposed
economy.

Second, dispersion appears to be greatest in industries which are highly concentrated
and which are either now highly protected, or were so until recently.

It is reasonable to expect dispersion to be greatest in industries which are sheltered
from domestic and international competition – that is, industries where
high entry barriers ensure that the inefficiencies encouraged by trade protection
are not rapidly undermined by competing domestic entry. This presumption is
strengthened by the likelihood that adjustment to reductions in assistance
will be a long and drawn-out process in industries in which sunk costs are
high (since these make exit costly). The analysis bears this presumption out,
since the
intra-industry dispersion in relative unit costs is strongly positively
related to the product of the HHI and the ERA (the correlation coefficient
between these being
0.34).[26]

Third, the factors which most sharply differentiate firms within industries in terms of
their relative unit costs are intangible investment, specialisation and industrial relations.

This is suggested by the probit regression model for relative unit costs presented
in Table 8. Since a higher number implies lower costs than those of rivals,
positive (negative) coefficients refer to variables which
increase (reduce) competitiveness. Investment in knowledge (as measured
by the ratio of R&D to sales) and in people (as measured by training expense
relative to payroll) are more significant determinants of competitiveness than
are investments in automation, or the use of advanced technologies (which has
a negative, albeit insignificant, coefficient). While the evidence
is relatively weak, it is consistent with the argument that intangible assets
– such as skills and know-how – are idiosyncratic and difficult
to imitate, and hence provide a greater differentiating factor than do other
forms of investment.

Table 8: Relative Unit Costs Equation

Variable

Industry dummies

Industry averages

Constant

−1.91

(−3.70)

−2.64

(−3.02)

Industry dummies:

Food, beverages and tobacco

0.29

(0.99)

—

—

Textiles

0.80

(2.32)

—

—

Clothing and footwear

0.42

(1.34)

—

—

Wood products

0.85

(3.11)

—

—

Paper products

0.68

(2.23)

—

—

Chemical and petroleum products

0.43

(1.33)

—

—

Non-metallic mineral products

0.56

(1.55)

—

—

Basic metal products

0.16

(0.42)

—

—

Fabricated metal products

0.47

(1.88)

—

—

Transport equipment

0.50

(1.51)

—

—

Other machinery and equipment

0.32

(1.17)

—

—

Good enterprise agreement

0.18

(1.07)

0.18

(1.14)

Pay-for-performance

−0.003

(−1.92)

−0.003

(−2.09)

Advanced technology

−0.07

(−0.37)

−0.03

(−0.16)

R&D/sales

0.08

(1.75)

0.06

(1.55)

Size

0.02

(0.20)

−0.005

(−0.06)

Constraints on finance for capital

−0.48

(−3.52)

−0.50

(−3.73)

Too diversified

−0.23

(−1.75)

−0.25

(−1.93)

Too small

−0.37

(−2.46)

−0.43

(−2.99)

Utilise capital effectively

0.41

(2.94)

0.43

(3.24)

Training expense

0.05

(1.34)

0.05

(1.28)

Automation

0.07

(0.96)

0.07

(1.03)

Work team

0.15

(2.31)

0.16

(2.51)

Does not benchmark

−0.03

(−0.24)

−0.05

(−0.38)

Government as customer

−0.19

(−1.34)

−0.15

(−1.11)

No trade union

0.62

(3.83)

0.57

(3.75)

Industry average relative unit cost

—

—

0.46

(1.60)

Likelihood ratio

92.64

79.41

Per cent correctly predicted

73.63

73.15

Note: t-statistics are in parentheses.

The worst performers seem to be firms which feel they are too small, produce too
many products, and do not have the capital to expand. An inability to use capital
effectively, which is likely to be related to inadequate specialisation and/or
industrial relations constraints, is also a highly significant drag on cost
competitiveness. The results also suggest that firms without unions tend to
have significantly and substantially lower costs. For firms which do have unions,
having a ‘good’ enterprise agreement partly offsets the cost penalty.
Using work teams tends to reduce costs while pay-for-performance tends to increase
them (though the causality here may well run from having higher costs to adopting
pay-for-performance).

There is some evidence of intra-industry spillover, perhaps through demonstration
effects. This can be seen by replacing the industry dummies by the industry
average response to the question on relative unit costs, as is done in the
second column of the table. The other coefficients remain stable while the
industry average term is positive and significant at just over the 10 per cent
level on a 2-sided test.

Fourth, one aspect of the disparities in the firm performance is the
presence of a large tail of firms – accounting for just under 30 per cent
of firms and 20 per cent of employment – which carries out little or no
R&D, undertakes no benchmarking and does not export.

These firms are most likely to be selling intermediate inputs, generally in regional
markets. Typically they also have poorer cash flow than other firms and lower
(self-assessed) rates of growth of productivity.

Fifth, export competitiveness at the level of the firm appears to be
strongly influenced by relative unit costs, but is also affected by size,
ownership, benchmarking, technological capability and emphasis on quality.
As has been argued above, many of these variables ultimately seem to hinge
on the firm's exposure to, and willingness and ability to learn from,
world-best practice.

Table 9 reports a probit model on export orientation, defined as whether a firm lists
a foreign market as among its top two priorities. All the variables have the
expected sign, and the model correctly predicts, within sample, over 72 per
cent of the observations.

Table 9: Export Equation

Variable

Probit estimate

Constant

−3.34

(−5.39)

Industry dummies:

Food, beverages and tobacco

0.30

(1.33)

Textiles

0.69

(2.26)

Clothing and footwear

0.44

(1.67)

Wood products

0.17

(0.69)

Paper products

−0.59

(−2.24)

Chemical and petroleum products

0.27

(0.99)

Non-metallic mineral products

−0.64

(−1.86)

Basic metal products

0.07

(0.23)

Fabricated metal products

0.35

(1.63)

Transport equipment

0.27

(0.92)

Other machinery and equipment

0.17

(0.75)

Foreign ownership

0.44

(2.90)

Parent restrictions

−0.28

(−1.85)

R&D/sales

0.17

(4.89)

Size

0.15

(2.15)

Training expenditures/payroll

0.12

(0.90)

Customer relation is top priority

0.05

(1.16)

Customer complaint resolving process

0.02

(0.32)

Quality is top priority

0.07

(1.15)

Suppliers located overseas

0.30

(1.94)

Measures quality of output

0.26

(3.24)

Far away from quality certification

−0.02

(−0.39)

Possesses an advanced technology

0.47

(3.04)

Does not benchmark

−0.27

(−2.40)

Has an enterprise agreement

0.19

(1.55)

Defect/volume rate

−0.10

(−1.13)

Relative unit costs

0.21

(3.46)

Likelihood ratio

193.23

Per cent correctly predicted

72.46

Note: t-statistics are in parentheses.

Even correcting for other factors, large firms are more likely to be exporters than
are small firms. This confirms the results of the cross-tabulation analysis,
which showed that 46 per cent of the firms with more than
100 employees were export oriented, as compared to under 25 per cent
of those in the smallest size class (50 employees or less). Foreign ownership
also remains a significant factor increasing export orientation. However, the
effect can be offset, at least partly, by parent company restrictions; respondents
stating that they were subject to such restrictions having significantly lower
export propensities. As noted above, 37 per cent of the foreign firms reported
being subject to parent company restrictions – those doing so comprising
47 per cent of the US-owned firms, 33 per cent of the UK-owned firms but only
18 per cent of the much smaller number of Japanese-owned firms. These differences
may be related to differences in access to parent-company technology, but it
has not yet been possible to test this hypothesis.

Technological capability, as measured by the ratio of R&D to sales and by possession
of an advanced technology, has an effect on the propensity to export, above
and beyond its effect on relative unit costs. In addition to product differentiation
this may also be because firms which invest heavily in technical know-how are
more likely to be aware of broader market trends.

Quality also appears to play a significant role in export orientation. Here too the
commitment to monitoring performance – proxied in this context by whether
the firm systematically measures the quality of its products – seems
particularly important. Firms which benchmark are also more likely to be export
oriented, as are firms which are heavily involved with, and rely on, foreign
suppliers.

Finally, it is worth noting that when the other factors affecting export orientation
are taken into account, the Transport Equipment industry does not appear to
be especially export oriented – despite the large-scale export assistance
which this industry
receives.[27]
The dummy on Transport Equipment, though positive, is not higher than those
for a range of industries which are much less heavily assisted.

Sixth and last, the processes discussed above appear to have been paralleled
by a move to greater specialisation within industries, presumably reflecting the
sorting out of ‘good’ from ‘bad’ firms, and the elimination
of excess product variety.

The AMC survey itself does not provide information on changing patterns of intra-industry
specialisation. Nonetheless, an indicator of the trends in this respect can
be obtained by examining trends in intra-industry trade, since they can be
expected to capture the survival, and perhaps expansion, of those products
within each industry in which Australian firms are competitive, and the contraction,
and perhaps disappearance, of those in which they are not.

Two approaches have been used to examine trends in intra-industry trade. The first
relies on the separation of imports into ‘competing’ and ‘non-competing’
classes (the former referring to imports which are similar to goods produced
domestically, and the latter, to those which are
not).[28]
Madge, Bennett and Robertson (1989) present data on the ratio of ‘competitive’
to total imports from 1973 to 1987. These results confirm the
intra-industry
specialisation hypothesis – the ratio fell in 28 out of 41 3-digit industries
examined. However, in a small number of cases, the largest falls in the ratio
were experienced in the late 1970s with some beginning to rise towards 1987.
The absence of more recent data inhibits identification of whether this is
a change in the trend of the series.

The second approach relies on trade data reclassified into industry categories to
calculate Grubel-Lloyd indices of intra-industry
trade.[29]
The results are set out in Figure 11, first calculated on the basis of volume data
at the
2-digit level beginning in 1978, and then using data expressed in current
values at the 4-digit level for the period from 1981/82 (highly disaggregated
data not being available prior to that date on an industry basis). While it
is clearly preferable to work with volumes, the value data are subject to less
aggregation bias. Indeed, aggregation can produce very large differences in
the level of the series, in particular for textiles and wood products. However,
aggregation has less of an effect on changes in the indices. Both series provide
strong support for the hypothesis of increased intra-industry specialisation,
with the Grubel-Lloyd indices rising for almost all industry groups.

4.3.6 Concluding remarks

Taken as a whole, these results point to far-reaching change. Change, however, is
not an objective in its own right; rather, it is valued because it contributes
to improved productivity, and thereby creates scope for sustainable increases
in living standards. It is, after all, an essential part of the case for trade
liberalisation that, in the words of the seminal paper by Samuelson, ‘although
it cannot be shown that every individual is made better off by the introduction
of trade, it can be shown that through trade every individual could be better
off, or in the limiting case, no worse off’ (Samuelson 1939, pp.
204).[30]

The evidence reviewed above cannot prove that the changes which have occurred pass
the compensation test of welfare economics. Considerable adjustment is indeed
underway, and its main features seem consistent with the broad goals of reform,
but at least three observations need to be made.

First, there are obvious methodological limits to the analysis. As has already been
noted, the cross-sectional relationships examined through the AMC survey are
inherently complex, and further exploration of their causal structure is needed
before it can safely be concluded that altering the trade exposure variables
would produce the desired changes in performance.

Second, even with the rather large datasets available, there are many things which
remain unexplained. It is a familiar finding of empirical research at a micro
level that the factors which distinguish more from less successful organisations
cannot be pinned down completely, regardless of how many control variables
are factored into the
analysis.[31]
For example, 30 years of research on the factors determining school performance
has tended to converge on the conclusion that the key element is leadership
– a conclusion which, though it would hardly have been unpalatable to
economists as diverse as Marshall, Pareto and Schumpeter, provides little comfort
to would-be social engineers.

Third, the findings provide only limited support for those versions of the analytical
arguments, summarised at the start of this section, which emphasise the role
of intensified competition in generating increases in productive
efficiency.[32]
Competitive conditions, though, do play a significant role in determining product
quality and in reducing intra-industry dispersion in performance. However,
by far the strongest relationships found in this paper link superior performance
to export orientation – not to competition per se.
Several factors may be at work.

This relationship may partly reflect the lack of an accurate indicator of the market
conditions facing individual firms and, in particular, of the intensity of
competition. Though this explanation is attractive, the range of indicators
tried suggests that it is not very
powerful.[33]

To the extent that competition affects all firms in an industry, but exporting only
affects those which have superior features and are best placed to adjust,
we would expect the link to other dimensions of performance to be stronger
for the latter, than for the former. However, this explanation, though attractive,
may have less power than it seems. After all, especially when set against
such a large sample, it would merely point to a weaker relationship between
competition and performance than that found between export orientation and
performance – not to an absence of a relationship
altogether.[34]

More plausibly, export orientation involves a substantial amount of self-selection:
it is presumably the ‘better’ firms which accept the challenge
of entering markets overseas. The fact that the links to performance seem
to depend on whether or not the firm is export oriented, rather than on the
amount of its exports, supports this view of export orientation as a shift
variable signalling better managerial
quality.[35]
At the same time, of course, exporting very probably exposes the firm to new
learning opportunities, which then serve to make the better firms even stronger.

Finally, it may well be that export orientation pays particularly large dividends
in terms of performance and notably in terms of
learning.[36]
This has often been suggested in the context of the dynamic Asian economies, and
may plausibly also have been at work in the productivity surge in post-war
Europe.[37]
Much as it is sometimes claimed that successful industrialisation in a number of
East Asian countries combined protection of the domestic market, with strong
inducements to export, this effect may be quite independent of competition
in the domestic market. Rather, the argument runs that the (at least partly)
sheltered conditions in the domestic market provided firms with the resources
required to compete vigorously overseas. The productivity benefits of the
latter, outweighing the costs of the former, the outcomes were supportive
of rapid economic
expansion.[38]

5. Policy Implications

In market economies, firms are the primary focus of activity. Yet firms differ greatly,
and in ways which are often difficult to explain. A great deal of recent work
in economics emphasises this heterogeneity within industries, and tries to analyse its causes and consequences.

The work reported on here also emphasises these differences, which then play a major
role in understanding the response to the increasing international integration
of the Australian economy.

Seen from a societal point of view, many of these differences in firm behaviour are
inevitable and some are positively desirable – since it is impossible
to know in advance which response to change will ultimately prove most successful.
Nonetheless, the persistence of a large tail of firms which seems to operate
far from best practice, makes little effort to monitor efficiency and has little
involvement in the international economy, could be a cause for concern.

It is interesting in this respect to compare the replies to the AMC survey with those
of a recent, and as yet unpublished, survey of manufacturing firms carried
out by the World Bank. In particular, in the World Bank replies for Japan,
Korea and Taiwan, the gap in performance between the firms which considered
themselves as technology leaders and others is considerably smaller than it
appears to be in Australia.

In part, this may simply reflect different points on the adjustment path –
and so be viewed as a problem which will prove largely self-correcting. But
it may also be amenable to policy action aimed at accelerating the rate at
which large gaps between best and average practice are
narrowed.[39]
Three options, which might be seen as mutually reinforcing, may be identified in
this
respect.[40]

A first is to ‘toughen’ the selection environment in which firms work.
The evidence reviewed above does provide some support for this option, since
there is a relationship between the intensity of competition and the extent
of inter-industry dispersion in firm performance (although not the level of that performance). Going by the AMC survey, many of the ‘lagging’
Australian firms survive in regional markets, where competitive disciplines
are most likely to be weak. While competition cannot be said to be a panacea,
further progress in removing the impediments to trade between the States should
induce greater and more rapid change among this part of the corporate population
– including exit by those firms whose long-run prospects are
poorest.[41]

A second option is to seek to strengthen the capabilities of lagging firms to catch-up.
Access to technical competence is a case in point. A very high fraction of
Japanese, Korean and Taiwanese firms surveyed by the World Bank make significant
use of industrial extension services and of practically-oriented technical
institutions (such as the Prefectural Laboratories in Japan or ITRI and CPC
in Taiwan). These too are surely no panacea, but there may well be lessons
here for making more effective use of the resources currently devoted to National
Industry Extension Service and to the CSIRO.

Finally, a third option involves better identifying and easing the obstacles firms
face to greater involvement with international markets. A specific question
in this respect is asked in the AMC survey: and a major obstacle identified
related to exchange rate uncertainty – with export-oriented firms seeing
this as a greater problem than did their domestically-oriented counterparts.
It is perhaps too easy to dismiss these views as reflecting a lack of understanding
of the relevant options – after all, there is no reason to view firms
as more ignorant in this respect than they are in others. The challenge then
is to take these perceptions seriously, while recognising that for this problem,
as for the others dealt with in this paper, there are simply no magic answers.

Appendix A: Trade Equation: Concepts, Sources and Methods

The equation was estimated using data for 1975, 1980, 1985 and 1990 by SUR. Inspection
of the data suggested that different country groups had different intercepts,
and that the relation between protection and trade intensity also differed
across country groups. As a result, shift and slope dummies were included
to allow for these different relationships. The differing relationship between
protection and trade intensity may reflect the inadequacy of our measure
of the former (based on tariff-receipts data) and regional differences in
non-tariff barriers.

Across and within equation restrictions were tested. The restrictions that the coefficients
on real GDP and real GDP per capita are equal across time could not be rejected
and, as a result, were imposed.

A number of different specifications of the ‘proximity’ variable were
tested as shown in Table A1. The specification used in the estimates presented
in the text is given by:

where distance is the airline distance between major cities. The variable measures
the sum of world output discounted by distance – the square of the
log of these figures is then taken to allow for non-linearities in the
relationship.[42]
Of the other measures, a number have been commonly used in the literature –
for example Lawrence (1987) uses the log, and square of the log, of the fifth
measure in the table. In addition, in order to allow for non-linearities
in the relationship between distance and transport costs, the variables were
entered in a number of different ways. It was consistently found that the
specification of this variable had little impact on the results.

Table A1: Alternative Measures of Proximity

Formulae

How it entered the equation

PROXj

ln(PROXj)

ln(PROXj), [ln(PROXj)]2

ln(PROXj)

ln(PROXj), [ln(PROXj)]2,
[ln(PROXj)]½

PROXj, ln(PROXJ)

Proximity variables on a regional basis were also constructed but were of limited
success in estimation.

Where available, data were collected for the 66 countries set out in Table A2. Singapore
was removed for the estimation because it was considered an outlier and was
thought to influence the results. Senegal, Tanzania, Zimbabwe, Bangladesh,
Malaysia, Yugoslavia, Iran, Honduras and Jamaica were excluded due to incomplete
data. The model was then estimated for 56 countries. In the estimation, logs
were taken of trade intensity, real GDP and real GDP per capita.

Trade intensity is defined as (all in nominal
values), and is the variable ‘OPEN’ in the Penn World Tables
(5.5) (PWT).

Real GDP per capita in constant dollars (chain index) is the variable ‘RGDPCH’
in the PWT.

Real GDP is calculated by multiplying real GDP per capita by population which is
the variable ‘POP’ in the PWT.

The distance data used to construct PROX were obtained from the Macintosh Map. The choice of major cities
follows Frankel and Wei (1993). Where a country was not included in Frankel
and Wei, the city with the largest population was used.

The variable measuring the degree of protection is defined as the ratio of customs
duty (in domestic currency) to manufacturing imports (in domestic currency).

Data on customs duty in domestic currency were taken from the IMF Government Finance Statistics
(various issues). Breaks in the custom data series may affect the 1975 observation
for Denmark, Italy and the
United Kingdom. The 1990 observation was unavailable for Argentina,
Barbados, Canada, Chile, Colombia, Guatemala, New Zealand and South Africa
and was replaced, where available, with the 1989 observation. For Chile and
New Zealand the 1988 observation was used. In some cases customs data had
to be adjusted for changes in the currency of denomination.

Data on manufactured imports in domestic currency used in the construction of the
protection variable were taken from the UN Yearbook of International Trade Statistics, Volume 1 (various years).
Manufactured imports are defined as BEC categories 4,5 and 6. Manufactured
imports, rather than total imports, were used in the construction of the
protection variable, given the perception that quantitative barriers were
more common on non-manufactures (excluding textiles and apparel). However,
the correlation coefficient between this measure and one based on total imports
is above 0.9 in all years.

The use of a customs-based measure for protection is less than ideal. Dornbusch (1993)
identifies a number of problems with such a measure. First, as elasticities
of demand and supply vary over goods, an aggregate customs measure gives
a poor indication of the marginal protective effect of a tariff. Second,
it ignores the effects of protection on intermediate inputs and, third, it
ignores non-tariff barriers. Prohibitive tariffs will also be understated
using such a measure. Of particular concern is the possibility that as, over
time, quantitative barriers are replaced by tariffs, these measures will
imply rising protection.

Appendix B: Measures of Export Orientation and Import Penetration

When examining the openness of goods markets, it is common to look at the extent
to which imports account for the supply of goods to the domestic market (their
share in apparent consumption), and similarly, the proportion of domestic
sales that is exported (see Industries Assistance Commission (IAC) (1985)
and Gruen (1985)).

There are a number of problems with this sort of analysis. The first concerns the
treatment of own-industry intermediate input in the construction of the domestic
sales measure. This problem has two parts. First, these measures typically
double count own intermediate input (OII). This can lead to a substantial
under estimation of the level of orientation and penetration measures in
sectors where own intermediate input usage is high – for example, in
textiles, wood products, paper products, non-petroleum based chemicals, and
basic metal products, own-industry intermediate input makes up over one fifth
of ‘gross’ output (in an input-output sense) or sales. Further
it double counts imported own intermediate inputs. Second, because the measures
include OII when it is traded between establishments in an industry, but
not when it is traded within the establishment, the level of the series is
sensitive to the definition of ‘establishment’ used.

As a result of the first problem, estimates of the share of own intermediate input
in gross output have been calculated from the 1989/90 input-output tables
and used to adjust the level of turnover/sales used to calculate the penetration/orientation
measures. The resulting change is most significant in those industries listed
above where own intermediate input is a large proportion of gross output.

The second problem with the conventional analysis revolves around whether sales or
turnover data are used, the former being available from a quarterly survey
of business, and the latter from the Census of Manufacturing. Although the
measure of sales is probably closer to the desired concept, the fact that
turnover is a result of the Census and is thus likely to be more accurate
means that it is used below. These data were also preferred in the IAC/Gruen
series and so make the series constructed here more comparable to the earlier
work. It should be noted that because sales is a narrower concept that turnover,
measures created using the former data would lead to higher measures of import
penetration and export orientation. In years where the Census was not undertaken,
sales data from the Manufacturers Stocks and Sales release has been used
to interpolate. The deflators used to construct constant price sales data
have also been used to deflate the nominal turnover data (this is the process
adopted by the ABS in the construction of constant price product data).

One final problem worthy of note relates to the ABS definition of activity undertaken
in a business. Importantly, production undertaken by a business on commission
for another company using that other company's own
inputs (intermediate only) is not counted as production by that firm.
However, the commission earned is included in turnover. The ABS believes
that this could be a problem in the clothing and petroleum refining
sectors.[43] The
problem can be corrected for the petroleum refining industry because the
major refiners changed the way they operated between 1988/89 and 1990/91
and began reporting the value of goods produced under commission for their
parents as gross output. As a result, the ABS believes that turnover figures
from 1990/91 accurately reflect the value of production by the industry.
Nominal turnover data are then extrapolated backwards using volume measures
of production (taken from ABARE) inflated using the APMI for petroleum products
(this is the method used by the ABS to construct constant price value added
data for the sector). However, it is impossible to make similar adjustments
for the clothing data and so the level, if not the growth in, the series
for this industry must be interpreted with caution.

In summary, import penetration has been calculated by taking f.o.b. total imports
as a ratio to an estimate of the size of domestic sales. The latter has been
calculated as turnover by domestic firms, less exports f.o.b., plus imports
f.o.b., less an adjustment for own intermediate input to remove double counting
of both own intermediate input produced at home and that which has been imported.
Export orientation has been calculated as the ratio of exports f.o.b. to
turnover of domestic firms, adjusted similarly for own intermediate input.
All of the above uses volumes data. The adjustment for OII has been taken
from the 1989/90 national accounts (which is the base year for constant price
estimates). This has been calculated as advocated in Chapter 19 of the Australian
National Accounts: Concepts Sources and Methods on Input-Output Tables (paragraph
19.34).[44]
Using a table which allocates competing imports indirectly, the diagonal
of the 1st quadrant (that containing own-industry intermediate input) has
been used to obtain an adjustment factor for gross turnover. As a consistency
check, the resulting series were then compared with those derived by IAC/Gruen
– not surprisingly, given the adjustment, the constructed series is
above that of the IAC/Gruen in every case but that of petroleum where a different
turnover series has been used. The petroleum refining volumes series was
compared to a series derived from ABARE data and was found to be very
similar.[45]

Appendix C: Construction of Approximate Herfindahl Indices

The approximate Hirschman-Herfindahl indices (HHI) referred to in the text have been
constructed using
trade-adjusted concentration ratios by two methods described in Schmalensee
(1977). The two methods (‘MIN’ and ‘MINL’ in Schmalensee's
terminology) were ranked in the top 5 out of 12 plausible surrogates presented.
The method MINL was the computationally least demanding of the top two surrogates
and is the basis for the results presented in the
text.[46]

The starting point for these measures is data on concentration ratios published by
the ABS. The concentration ratio corresponding to the share in some measure
of activity, A, of the firms in the k'th rank, is given by where Ak
is some measure of activity for the firms in the k'th rank (in the analysis
below, the measure is turnover or sales). In a closed economy context, the
ratio in terms of sales would take total sales of the four largest firms
as a proportion of the entire sales of the industry (note that the familiar
double counting of own industry intermediate input arises – this is
abstracted from below). When allowance is made for the open economy, the
relevant measures become where X
represents exports, M imports, S sales. Unfortunately,
there is no data source on exports by size of firm constructed on the same
basis as these figures, and so it is usual to assume that exports of the
firms in the k'th rank are in proportion to their share of total
sales,
or . X (Clark
1985). The ‘trade-adjusted’ (superscript ‘ta’)
concentration ratio for the k'th rank can then be written as
Obviously, if
larger firms are more likely to export, measures constructed on this basis
will overstate concentration in the industry. Another problem with the figures
is that it makes no distinction between total imports and those that are
‘competing’. In a number of industries it is believed that domestic
manufacturers are substantial importers of products and so not attributing
these to the firms themselves will understate their market power (offsetting
the bias in the export figures).

It is possible to then approximate the HHI using the trade-adjusted concentration
ratios. The simplest method, Schmalensee's MIN, is to take the average share for each class size (the Australian
data presents information on the market shares five ranks, each of four firms,
plus a remainder) given by where Nk
is the number of firms in the k'th rank, and then to construct
the approximate HHI as where K
is the total number of ranks.

It is important to note that the figure MIN represents the minimum value the HHI
could take, given the concentration ratio data, because firms within ranks
are assumed to have exactly equal market shares. Importantly, if the degree
of equality in market share within ranks varies over time, then the magnitude
by which MIN understates the true HHI will also vary. The other measures
presented in Schmalensee (1977) involve making various assumptions about
how market share varies within ranks. Given that the largest firms have the
largest weights within the index, the methods which vary the share of the
top ranks of firms have the most effect. The method Schmalensee denoted MINL
makes the assumption that all firms in ranks, other than the top rank, have
shares equal to the average for that rank, however, the shares within the
top rank varied linearly with the smallest having the same share as the average
for the second rank. This proxy is calculated as which shows that
it always greater than the proxy MIN, and that the difference between the
two measures depends on the squared difference between the average shares
of the top two ranks.

Footnotes

The authors are especially grateful to Bill Mountford, Director of the Australian
Manufacturing Council, for providing access to the results of the survey
analysed in this paper. Anna Slomovic of RAND, and Philip Lowe of the RBA,
provided helpful comments on an earlier draft; Scott Austin, Matthew Boge,
Brian Brooke, Lynne Cockerell, Christine Groeger, Alex Heath, Eric Ralph,
Mary Savva, and Geoff Shuetrim provided valuable assistance. However, the
authors have sole responsibility for the views expressed.
[*]

Estimates of the reduction in British protection following the repeal of the Corn
Laws depend on the treatment of terms of trade changes. Data in Imlah (1958)
on wheat prices prior to repeal imply a domestic price wedge before transport
cost in excess of 50 per cent of the world price. However, as the elasticity
of foreign supply was low, the effect of repeal in 1846 was to sharply increase
world prices. McCloskey (1981) argues that the terms of trade effects were
so great as to actually reduce British national income, the original tariff
having been close to the ‘optimal tariff’. McCloskey's ‘best’
estimate of the extent of the tariff reductions is in the order of two-thirds.
[1]

At least for industrial countries. Havrylyshyn (1990) notes that the proportion of
growth explained by capital accumulation is generally much higher for developing
countries.
[3]

It is worth noting, however, that ‘… the ratio of exports or imports
to national income overstates the relative importance of trade in domestic
economic activity, increasingly so as the import content of exports rises.
[Moreover], insofar as the proportion of traded goods destined for intermediate
use varies between countries and over time, the ratio is an unreliable guide
to either the ranking of countries by the relative importance of trade or
to trends over time in the importance of international trade relative to
total domestic output’ (Blackhurst, Marion and Tumlir 1977, p. 18).
[4]

Note that these data are nominal, and thus vary from those presented in Figure 1.
[5]

As a ratio to cumulated GDP, the respective figures were 1.9 and 1.1 per cent. The
rise is despite regulatory changes which removed the bias against portfolio
investment.
[6]

Graham and Krugman (1993) argue that this is a better measure of the extent of foreign
direct investment than statistics based on balance of payments data. For
a more detailed discussion of trends in Australian FDI see the paper by Howe
in this Volume.
[7]

Using shares in annual FDI in the United States over the period 1976 to 1992 as the
dependent variable, the regression line is given by:

where: DIST is distance of capital city from Washington, DC; PCAPGDP is per capita
GDP at 1985 prices and current exchange rates; GDP is GDP in 1985 prices;
R&D is Business Enterprise Outlays on R&D at 1985 prices; EXCHRATE
is the percentage change in the US exchange rate over the previous five
years; and t-statistics are in parentheses. Normalised by the mean error,
the average gap between the actual and predicted value for Australia's
share over the period to 1982 was −0.55; for the period from 1983
to 1992 it was +2.38.

Country j's RCA in industry i is given by where X
denotes exports.
[9]

Due to Lawrence (1984), the index measures structural change between two points in
time as where s
denotes share of, in this case, either value added or employment. A value
of zero implies complete stability in the economy's structure, whilst
a value of one implies a complete turnover.
[10]

This indicator was first presented in OECD (1987). An update is provided in Meyer-zu-Schlochtern (1994). The index is constructed by using the RAS adjustment
procedure, commonly employed in updating input-output tables, to calculate
‘expected’ values of sector shares – that is, the values
which would be observed if the sector share within each country evolved according
to that sector's growth rate across all countries, corrected for the
ratio of the economy-wide growth rate in that country, to the overall growth
rate for the grouping of countries as a whole. The sum of the absolute values
of the differences between the actual and the expected average annual growth
rates for each country provides a measure of the extent of structural change
in that country.
[11]

The values of the index, calculated solely for manufacturing over the period from
1970 to 1989,
are (setting Australia at 100), Sweden 196, UK 211, Canada 139, Japan
107, US 80. The index here is defined as: I = sum (absolute values [expected
minus actual annual average growth rate of employment]) where the sum is
taken over the following industry groups: Basic Metals, Food, TCF, Wood and
Wood Products, Pulp and Paper, Chemicals, Non-Metallic Mineral Products,
Fabricated Metal Products, Machinery and Equipment and Means of Transport.
[12]

The decomposition arises by noting that manfacturing labour-productivity growth in
period 1 is approximately equal to the sum of the changes in individual industries
labour productivity levels weighted by their start-of-period share of total
hours worked (hsi,0) plus the sum of the changes in their
weights multiplied by their start-of-period labour-productivity level (pi,0)
relative to the aggregate (P0), or [13]

That is, correlation of ‘environmental’ factors. Correlation of managerial
talents may inhibit the monitoring process as managers free ride on each
other's performance. See Vickers (1994) for an overview.
[14]

Lee (1994) presents evidence that per capita income growth rates are positively related
to the ratio of imported to domestically produced capital goods. See also
Coe and Helpman (1993).
[15]

There is considerable evidence on the impacts of internationalisation on price-cost
margins. See, for example, Schmalensee (1989) and the studies referenced
in Jacquemin and Sapir (1991) which are in the tradition of the older ‘gross
margins’ literature, or the ‘new empirical industrial organisation’
studies of Levinsohn (1993) and Harrison (1994).
[16]

Rodrik (1988) presents a model in which liberalisation, by reducing market share,
reduces the incentive for the firm to make productivity improving investments.
[17]

This is not the only factor at work. Reduced shirking may also arise in principal-agent
models from the effect of intensified competition on the incentives of owners
and managers to trade-off the incentive and insurance components of the contract
between them, for example by altering the cost of slack. Thus, Horn, Lang
and Lundgren (1991) develop a model which allows for international trade
but in which all potential avenues for gains from trade, other than those
associated with agency costs, are excluded. In this model, managers affect
productivity as their effort is assumed to increase the productivity of labour
– that is, to lead to a more efficient organisation of production.
International trade then has an impact on the trade-off in the agency relation
in two ways: it increases the perceived price elasticity of demand, which
increases output and the incentives for owners to be tough; and it increases
the demand for labour which increases real wages, and so adds further incentive
to economise on labour by increased managerial effort. For models with similar
mechanisms at work, see Horn, Lang and Lungdren (1990, 1994). In these models,
competition can increase managerial effort without necessarily decreasing
the degree to which the effort supplied is inefficient.
[18]

Note that although many of the tau values seem small, this does not imply anything
about the strength of the correlation.
[19]

Import penetration measures at the industry level are likely to be too aggregated
to adequately capture competitive conditions in particular product markets.
These measures will overstate the degree of product market discipline exercised
by trade flows when the imports in question are non-competing – be
it because the incumbent domestic producers are the main importers (as is
the case, for example, for paper) or because the imports are highly differentiated
relative to domestic output. For example, Messerlin (1993) finds for France
that domestic manufacturers account for
70 per cent of imports of home appliances, and for between 20 and
50 per cent of imports of textiles and apparel. Also see Utton and Morgan
(1983). Equally, the measures will understate the disciplines trade imposes
when the supply elasticity of imports at the margin is high – as may
be the case in industries where competition occurs primarily on the basis
of costs and where a few large retail chains account for a large share of
purchases.
[20]

Until March 1992, the survey was of Victorian manufacturers. ASIC 2-digit figures
for this period have been reweighted at the 3-digit ASIC level using the
1989/90 input-output tables.
[21]

On international trade and contestability, see Baumol and Lee (1991).
[22]

We have relatively little faith in the MES estimates which were adapted from Mueller
and
Owen (1985), often augmented by arbitrary assumptions. However, the
same results hold even if this variable is excluded from the analysis.
[23]

The overall level of industry demand, on the other hand, might be more stable, if
cyclical positions internationally are not fully synchronised. Even so, given
the impact of market widening on demand elasticities, the demand facing individual
firms would probably become less predictable, all the more so once firms
had lost the cushion of ‘made to measure’ protection.
[25]

In a stochastic frontier production function study of technical efficiency in Australian
manufacturing, Harris (1992) finds that tariffs increase the intra-industry
dispersion in efficiency. This finding is confirmed for some other countries
in Caves (1992a, 1992b).
[26]

According to the Industry Commission (1993), outlays on the Passenger Motor Vehicle
Export Facilitation Scheme were likely to amount to some $180 million in
1993–94, absorbing just over
20 per cent of outlays on specific export facilitation and assistance
programs and 14 per cent of outlays on all export-related programs.
[27]

Such a distinction is used in the ABS input-output tables, and by Industries Assistance
Commission (1985) and Madge, Bennett and Robertson (1989).
[28]

It is worth noting that even in general equilibrium this result does not hold when
competition is imperfect (Ventura and Cordella 1992).
[30]

There may be a ‘40 per cent’ rule in this respect. Cross-sectional studies
in areas as diverse as the explanation of wage structures, of educational
outcomes and of the probability of bankruptcy typically explain no more than
40 per cent of the variance in the dependent variable. In other words, 60
per cent of the variance is within cell, almost regardless of the number
of cells. The results presented here are usually well above and rarely below
this benchmark.
[31]

These findings echo those of Nickell (1993).33. It is, however, worth noting that
rather similar studies for Japan, Taiwan and Korea, but which relied on questionnaires
in which firms were asked to rank the intensity of competition they faced,
did find a relation between the effort firms made in searching out external
sources of information and the intensity of competition. See respectively
Yoshitaka Okada, Interactive Learning and Techno-Governance Structures (manuscript)
April 1994; Gee San, Study on Policy and International Priorities for Technology
Development: The Case of Taiwan (manuscript) April 1994; Kee Young Kim, Policies
and Institutions for Industrial and Technological Development: A Korea Study
(manuscript) June 1994.
[32]

It is, however, worth noting that rather similar studies for Japan, Taiwan and Korea,
relied on questionnaires in which firms were asked to rank the intensity
of competition they faced, did find a relation between the effort firms made
in searching out external sources of information and the intensity of competition.
See respectively Yoshitaka Okada, Interactive Learning and Techno-Governance
Structures (manuscript) April 1994; Gee San, Study on Policy and International
Priorities for Technology Development: The Case of Taiwan (manuscript) April
1994; Kee Young Kim, Policies and Institutions for Industrial and Technological
Development: A Korea Study (manuscript) June 1994.
[33]

For example, we find no link between competition and productivity growth, automation,
most dimensions of time spent reviewing business performance, or likelihood
of export-orientation.
[34]

This is consistent with recent work on R&D which finds greater differences between
those firms which carry out R&D, and those which do not, than between
those which carry out some R&D, and some which carry out a great deal.
It can be hypothesised that much like exporting, the R&D variable is
picking up a greater interest and ability to learn about the outside world,
and hence to adjust promptly to change.
[35]

The general argument that export orientation is closely associated with productivity
growth and some supporting evidence is set out in Balassa (1988). Important
micro-level analyses are Dahlman, Ross-Larson and Westphal (1987) and Pack
(1988).
[36]

On East Asia, see World Bank (1993) and on Europe, see Mueller and Owen (1985) and
the results (which in several respects parallel those reported here) in Zimmerman
(1987).
[37]

See, for example, Wade (1992) (on Taiwan), Amsden (1989) (on Korea), and Samuels
(1994) (on Japan). The views expressed by these authors are controversial.
See also essays in Krause and Kihwan (1991).
[38]

Clearly, this gap may well be larger in a highly-dynamic economy than in one in which
change is proceeding slowly. Nonetheless, there is no evidence to suggest
that the larger gap observed in Australia arises from greater dynamism. Indeed,
going by conventional indicators such as the FMS indicator in the ISDB, the
rate of structural change in the Japanese and Taiwanese economies considerably
exceeds that in Australia.
[39]

The government's ‘industry plans’ each contain some mix of these
measures, though they generally place less weight on strengthening market
disciplines. The automotive industry plan, for example, appears to have resulted
in substantial improvements in some indicators – for example, physical
productivity, product quality and export orientation; but it also appears
to have been associated with a fairly sharp rise in motor vehicle prices
(Automotive Industry Authority 1993). It is arguable whether the ‘industry
plan’ model could, or should be, used more broadly. It seems vulnerable
to collusion and the problems which need to be tackled span so broad a range
of industries that a more horizontal and industry-neutral approach seems
preferable.
[40]

Given that many of the worst performers report poor cash flow, greater competition
is likely to substantially reduce their survival chances.
[41]

See Balassa (1986) for a discussion of the relationship between distance and transport
costs.
[42]

The problem understates the level of sales and thus overstates the orientation/penetration
measures.
[43]

Note that this paragraph discusses how to construct a measure of ‘net’
domestic output, which involves subtracting the value of domestically sourced
own intermediate input from gross output but leaving behind imported OII.
For our purposes, to avoid double counting imported OII, this is removed
as well.
[44]

ABARE use a different distinction between refined and crude petroleum than does the
ASIC. The ABARE data are from the Commodity Statistical Bulletin 1993, Table 269.
[45]

Madge, Bennett and Robertson (1989) present results using Australian data for the
MIN surrogate.
[46]

Frankel, J.A. and S.J. Wei (1993), ‘Is There a Currency Bloc in the Pacific?’
in A. Blundell-Wignall (ed.),
The Exchange Rate, International Trade and the Balance of Payments,
Reserve Bank of Australia, Sydney,
pp. 275–307.